A medication analysis method and system for Parkinson's disease patients
By obtaining medical records and living environment data of Parkinson's disease patients, and using drug association models to generate and adjust drug use plans, the problem of low efficiency of adjusting drug use plans in the existing technology is solved, the drug compliance and treatment effect are improved, and medical costs are reduced .
Patent Information
- Application Number
- CN202311748994.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-19
- Publication Date
- 2024-12-10
- Estimated Expiration
- 2043-12-19
AI Technical Summary
In the clinical use of drugs, it is difficult to efficiently and accurately adjust the medication regimen of patients with Parkinson's disease, resulting in large differences in patient tolerance and drug effect, affecting drug compliance and medical costs.
By obtaining the patient's medical record information and living environment data, the initial drug treatment plan is generated using the drug association model, and cluster analysis and adjustment are carried out based on the treatment results to generate target drug treatment plan suitable for the patient, filtering drugs that may affect the physical condition, assisting Medical staff analyze the changes in the condition.
It improves the personalization and efficiency of drug use plans, enhances monitoring and adjustment of patient responses, improves drug compliance and treatment effects, and reduces medical costs.
Smart Images
Figure CN117637100B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data processing, and in particular to a medication analysis method and system for Parkinson's disease patients. Background Art
[0002] Medication is the main treatment for Parkinson's disease. A study of the Medicare Part D population showed that higher adherence to anti-Parkinson's disease drugs was associated with lower total healthcare expenditures. Another questionnaire study showed an association between lower medication adherence and higher medication burden in elderly patients. There are many factors that affect medication adherence, such as patient characteristics, medication frequency, patient understanding of the disease, medication duration, medication quantity, medication packaging, and patient medical insurance. However, little is known about the impact of Parkinson's disease medication adherence and direct healthcare costs.
[0003] However, in clinical medication, patients have different tolerance and effects on drugs, so it is necessary to consider the patient's feedback on the drug after medication to make subsequent medication adjustments. Currently, patient feedback is mostly based on inquiries during ward rounds, and then compared with the patient's multiple test lists and medical records in a specific time period, and then medication adjustments are made based on the comparison results. This method is inefficient and cannot fully and accurately obtain patient feedback on the current medication plan.
[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention
[0005] The purpose of this application is to provide a medication analysis method and system for Parkinson's disease patients, which at least overcomes the problems existing in the prior art to a certain extent, generates a treatment plan for the patient through a drug association model, the patient's living environment factors and the patient's medical history information, and adjusts the treatment plan according to the patient's treatment results, thereby generating a target drug treatment plan suitable for the patient, further filtering drugs that may affect the patient's physical condition based on the patient's living environment factors, and assisting medical staff in analyzing the patient's condition changes and their causes.
[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present invention.
[0007] According to one aspect of the present application, a medication analysis method for Parkinson's disease patients is provided, comprising: obtaining the patient's medical record information and living environment data; processing the medical record information and the living environment data to generate an initial medication treatment plan; processing the initial medication treatment plan based on a drug association model to generate a target medication treatment plan; processing the patient based on the target medication treatment plan to generate a treatment result for the patient; performing cluster analysis on the patient's treatment result to generate abnormal risk reminder information; processing the patient's medical record information based on the abnormal risk reminder information to generate a newly added therapeutic drug; adding the newly added therapeutic drug to the target medication treatment plan to generate a new target medication treatment plan; processing the patient based on the new target medication treatment plan to generate a medication analysis result.
[0008] In one embodiment of the present application, the medical record information is processed to generate an initial drug treatment plan, including: obtaining therapeutic drugs corresponding to the medical record information; obtaining the patient's historical physiological disease information; obtaining risk factors between different therapeutic drugs based on a target drug risk model; and screening suitable therapeutic drugs based on the risk factors, the historical physiological disease information and the living environment data, and using them as the initial drug treatment plan.
[0009] In one embodiment of the present application, the initial drug treatment plan is processed based on the drug association model to generate a target drug treatment plan, including: obtaining the disease type of the medical record information; obtaining the medical record information of other patients with the same disease type; processing the initial drug treatment plan based on the medical record information of the other patients to generate drug recommendation information, wherein the drug recommendation information includes the frequency of occurrence and cost-effectiveness of different drugs; processing the initial drug treatment plan based on the drug recommendation information and the patient's historical physiological disease information to generate a target drug treatment plan.
[0010] In one embodiment of the present application, the patient is processed based on the target drug treatment regimen to generate the patient's treatment results, including: obtaining the patient's physiological state information and corresponding dietary information after each medication; obtaining the efficacy results of each drug based on the physiological state information; performing abnormality detection on the efficacy results to generate detection results; processing the drugs whose detection results are in an abnormal state based on the dietary information to generate drug efficacy prediction information; processing the target drug treatment regimen based on the drug efficacy prediction information to generate the patient's treatment results.
[0011] In one embodiment of the present application, the cluster analysis of the patient's treatment results is performed to generate abnormal risk warning information, including: generating effectiveness information of each target drug for the patient's pathological treatment based on the patient's treatment effect; dividing each target drug into preferred drugs and secondary drugs based on the effectiveness information; if the proportion of the secondary drugs is greater than a preset threshold, generating abnormal risk warning information.
[0012] In one embodiment of the present application, the cluster analysis of the patient's treatment results to generate abnormal risk warning information also includes: generating the drug resistance of each target drug at each treatment stage based on the patient's treatment results; generating a medication dependence threshold that affects the patient based on the drug resistance; if the medication dependence threshold is greater than the target threshold, generating abnormal risk warning information.
[0013] In one embodiment of the present application, the patient's medical record information is processed based on the abnormal risk reminder information to generate new therapeutic drugs, including: generating the patient's pathological treatment prediction results based on the patient's medical record information; processing the pathological treatment prediction results based on the abnormal risk reminder information to generate real-time pathological treatment results; generating new therapeutic drugs based on the real-time pathological treatment results, the patient's historical physiological diseases and the target drug treatment plan.
[0014] Another aspect of the present application is a medication analysis device for Parkinson's disease patients, characterized in that it includes: an acquisition module for acquiring the patient's medical record information and living environment data; a processing module for processing the medical record information and the living environment data to generate an initial drug treatment plan; processing the initial drug treatment plan based on a drug association model to generate a target drug treatment plan; processing the patient based on the target drug treatment plan to generate the patient's treatment results; performing cluster analysis on the patient's treatment results to generate abnormal risk reminder information; processing the patient's medical record information based on the abnormal risk reminder information to generate newly added therapeutic drugs; adding the newly added therapeutic drugs to the target drug treatment plan to generate a new target drug treatment plan; processing the patient based on the new target drug treatment plan to generate a medication analysis result.
[0015] According to another aspect of the present application, an electronic device is characterized in that it includes: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to implement the above-mentioned medication analysis method for Parkinson's disease patients by executing the executable instructions.
[0016] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a second processor, the above-mentioned medication analysis method for Parkinson's disease patients is implemented.
[0017] According to another aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a third processor, the above-mentioned medication analysis method for Parkinson's disease patients is implemented.
[0018] The present application provides a medication analysis method for Parkinson's disease patients, wherein the server obtains the patient's medical record information and living environment data; the medical record information and the living environment data are processed to generate an initial drug treatment plan; the initial drug treatment plan is processed based on a drug association model to generate a target drug treatment plan; the patient is processed based on the target drug treatment plan to generate the patient's treatment results; cluster analysis is performed on the patient's treatment results to generate abnormal risk reminder information; the patient's medical record information is processed based on the abnormal risk reminder information to generate a new therapeutic drug; the new therapeutic drug is added to the target drug treatment plan to generate a new target drug treatment plan; the patient is processed based on the new target drug treatment plan to generate a medication analysis result. The patient's treatment plan is generated through the drug association model, the patient's living environment factors and the patient's medical record information, and the treatment plan is adjusted according to the patient's treatment results, so as to generate a target drug treatment plan suitable for the patient, and the drugs that may affect the patient's physical condition are further filtered based on the patient's living environment factors, so as to assist medical staff in analyzing the patient's condition changes and their causes.
[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 A flow chart showing a method for analyzing medication for Parkinson's disease patients provided by an embodiment of the present application;
[0021] Figure 2 A schematic diagram of the structure of a medication analysis device for Parkinson's disease patients provided by an embodiment of the present application is shown;
[0022] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown;
[0023] Figure 4 A schematic diagram of a storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION
[0024] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.
[0025] Combine the following Figure 1 To describe the medication analysis method for Parkinson's disease patients according to the exemplary embodiment of the present application. It should be noted that the following application scenarios are only shown to facilitate understanding of the spirit and principle of the present application, and the embodiments of the present application are not limited in this regard. On the contrary, the embodiments of the present application can be applied to any applicable scenario.
[0026] In one embodiment, the present application also proposes a method for analyzing medication use in Parkinson's disease patients. Figure 1 The following schematically shows a flow chart of a method for analyzing medication for Parkinson's disease patients according to an embodiment of the present application. Figure 1 As shown, the method is applied to a server, comprising:
[0027] S101, obtaining the patient's medical record information and living environment data.
[0028] In one embodiment, the medical record information includes the visit date, number of visits, age, gender, etc. of the Parkinson's disease patient, determines the application details of anti-Parkinson's disease drugs for each Parkinson's disease patient within a preset time period, as well as the application details of the corresponding concurrent systemic diseases and anti-concurrent systemic disease drugs.
[0029] In another embodiment, living environment factors include, but are not limited to, the patient's daily eating habits, whether he or she has corresponding exercise habits, daily exercise data, sleep quality, living environment, mental state, etc.
[0030] S102, processing the medical record information and the living environment data to generate an initial drug treatment plan.
[0031] In one embodiment, the server will obtain the patient's physiological risk factors based on medical record information, obtain the correlation between each physiological risk factor and the occurrence of Parkinson's disease based on the living environment data, and sort them based on the correlation, so as to screen out the physiological risk factors that have the greatest impact on Parkinson's disease, and set a preset weight value for the patient's physiological state factors based on the sorting results. For example, if the patient lives in a place that is too humid or dry, whether the living air quality, sleep conditions, etc. will affect the patient's recovery or aggravate the physical condition, the weight value of the physiological risk factors that may affect the patient's physical state can be adjusted through the above considerations. In addition, the patient's home environment and living environment can also be improved according to the above situation, thereby improving the patient's quality of life.
[0032] In another embodiment, in addition, the server will also obtain the therapeutic drugs corresponding to the medical record information, as well as the patient's historical physiological disease information, obtain the risk factors between different therapeutic drugs based on the target drug risk model, and screen the appropriate therapeutic drugs according to the risk factors, historical physiological disease information and physiological risk factors, and use them as the initial drug treatment plan. The risk factor characterizes whether there is mutual exclusivity or other adverse reactions between different therapeutic drugs. The server will also obtain whether there are adverse effects between different physiological risk factors and different therapeutic drugs, and confirm the appropriate therapeutic drugs based on this. Among them, the target drug risk model can pre-train different drugs to see if there is a risk of medication conflict. The target drug risk model can be a ChatGLM-6B model or a GPT-4 model, etc. This embodiment does not specifically limit this. In addition, the drugs currently or in the future taken by the patient will be screened based on the patient's previous physiological diseases, and whether there is mutual exclusivity between different drugs will be analyzed. There is no risk of medication conflict between the corresponding drugs in the initial drug treatment plan, thereby ensuring the safety of the patient.
[0033] S103, processing the initial drug treatment plan based on the drug association model to generate a target drug treatment plan.
[0034] In one embodiment, the disease type of the medical record information is obtained, and the medical record information of other patients with the same disease type is obtained. Since each person's physical fitness is different, different people will have different symptoms when suffering from the same disease. Therefore, the medical record information is classified in advance. After the classification is completed, the symptoms existing under the same disease diagnosis result can be obtained, and the cause of the patient's illness can be further determined based on the existing symptoms, so as to facilitate subsequent drug recommendations. The initial drug treatment plan is processed based on the medical record information of other patients to generate drug recommendation information, wherein the drug recommendation information includes the frequency of occurrence and cost-effectiveness of different drugs. The initial drug treatment plan is processed based on the drug recommendation information and the patient's historical physiological disease information to generate a target drug treatment plan.
[0035] If it is detected that the frequency of a certain drug is higher than the frequency of other drugs for treating this type of disease, then consider whether there are other drugs with the same efficacy as this drug. If so, further confirm whether the alternative drug is mutually exclusive with other drugs taken in the patient's historical physiological disease information. If not, comprehensively consider the cost-effectiveness of the alternative drug. If the price is lower than the current drug, consider replacing the current drug with a higher frequency of occurrence with the alternative drug, and then formulate a target drug treatment plan suitable for the patient.
[0036] S104, treating the patient based on the target drug treatment regimen to generate a treatment result for the patient.
[0037] In one embodiment, the patient's physiological state information and corresponding dietary information are obtained after each medication, wherein the physiological state information includes one or more of electrocardiogram, blood pressure, blood lipids, pulse oxygen, blood ketones, blood uric acid, blood sugar, sleep conditions, brain waves, exercise conditions, height and weight. The efficacy results of each drug are obtained based on the physiological state information, and the efficacy results are detected for abnormalities to generate test results. If the value of the patient's physiological state information after taking the drug is greater than the normal threshold, it is determined that the current drug is abnormal. Drugs with abnormal test results are processed based on the dietary information to generate drug efficacy prediction information, and the target drug treatment plan is processed based on the drug efficacy prediction information to generate the patient's treatment results.
[0038] For example, the doctor will develop a complete treatment plan for the patient's condition, which may include several stages of treatment results, and set different treatment drugs according to different stages. If the patient's condition does not move in the expected direction of treatment results after taking the treatment drugs for that stage, a corresponding risk reminder message will be generated. In addition, the server will also obtain the patient's diagnostic information, and store the patient's health information and diagnostic information accordingly, so that medical staff can view the patient's health information and diagnostic information through the management platform, thereby assisting medical staff in analyzing the changes in the patient's condition and its causes.
[0039] S105, performing cluster analysis on the treatment results of the patient and generating abnormal risk warning information.
[0040] In one embodiment, the effectiveness information of each target drug for the patient's pathological treatment is generated based on the patient's treatment effect. The doctor will develop a complete treatment plan based on the patient's situation. The plan may include several stages of treatment effectiveness, and different treatment drugs are set according to different stages. If the patient's condition does not move in the expected treatment effect direction after taking the treatment drug of this stage, the target drugs that cause the current treatment effect to not meet expectations are analyzed, and the corresponding target drugs are divided into secondary drugs. Based on the effectiveness information, each target drug is divided into preferred drugs and secondary drugs. If the proportion of secondary drugs is greater than the preset threshold, abnormal risk reminder information is generated. If the proportion of secondary drugs is large, it proves that the current patient's treatment results have not reached the expected treatment goals. At this time, abnormal risk reminder information will be generated to assist medical staff in analyzing changes in the patient's condition and their causes.
[0041] S106: Process the patient's medical record information based on the abnormal risk reminder information to generate new therapeutic drugs.
[0042] In one embodiment, the patient's pathological treatment prediction results are generated based on the patient's medical record information, the pathological treatment prediction results are processed based on the abnormal risk reminder information to generate real-time pathological treatment results, and new therapeutic drugs are generated based on the real-time pathological treatment results, the patient's historical physiological diseases and the target drug treatment plan. The server analyzes the patient's historical physiological disease information, calculates the disease prediction results of complications, adds the disease prediction results including the patient's current disease and complications to the current patient's medical diagnosis information, extracts entities from the current patient's medical diagnosis information with the disease prediction results added, obtains the corresponding target drug treatment plan, performs entity similarity matching in the medical diagnosis knowledge graph according to the target drug treatment plan, and obtains the corresponding new therapeutic drugs.
[0043] S107, adding the newly added therapeutic drug to the target drug treatment regimen to generate a new target drug treatment regimen.
[0044] S108, treating the patient based on the new target drug treatment regimen and generating a medication analysis result.
[0045] In one embodiment, by continuously optimizing the target drug treatment regimen, a treatment regimen that is better suited to the patient is formulated, thereby assisting medical staff in analyzing changes in the patient's condition and its causes.
[0046] In this application, the server obtains the patient's medical record information and living environment data; processes the medical record information and the living environment data to generate an initial drug treatment plan; processes the initial drug treatment plan based on the drug association model to generate a target drug treatment plan; processes the patient based on the target drug treatment plan to generate the patient's treatment results; clusters the patient's treatment results to generate abnormal risk reminder information; processes the patient's medical record information based on the abnormal risk reminder information to generate new therapeutic drugs; adds the new therapeutic drugs to the target drug treatment plan to generate a new target drug treatment plan; processes the patient based on the new target drug treatment plan to generate medication analysis results. The patient's treatment plan is generated through the drug association model, the patient's living environment factors and the patient's medical record information, and the treatment plan is adjusted according to the patient's treatment results, so as to generate a target drug treatment plan suitable for the patient, further filter the drugs that may affect the patient's physical condition based on the patient's living environment factors, and assist medical staff in analyzing the patient's condition changes and their causes.
[0047] Optionally, in another embodiment of the method of the present application, cluster analysis of the treatment results of the patient to generate abnormal risk reminder information further includes:
[0048] generating drug resistance of each target drug at each treatment stage based on the treatment results of the patient;
[0049] generating a threshold of drug dependence affecting the patient based on the drug resistance;
[0050] If the medication dependence threshold is greater than the target threshold, abnormal risk reminder information is generated.
[0051] In one embodiment, the patient's dependence on each target drug is tested to determine the effectiveness of each target drug in subsequent therapeutic effects. For example, the patient could originally control the condition by taking one pill, but now needs to take two pills to achieve the same effect. However, two pills may cause greater damage to the human body or easily conflict with other drugs. At this time, for the sake of the patient's health, it is necessary to find a drug with the same efficacy and that will not cause other damage to the patient, and generate corresponding abnormal risk warning information.
[0052] By applying the above technical solutions, the server obtains the patient's medical record information and living environment data, obtains the therapeutic drugs corresponding to the medical record information, obtains the patient's historical physiological disease information, obtains the risk factors between different therapeutic drugs based on the target drug risk model, screens suitable therapeutic drugs according to the risk factors, historical physiological disease information and living environment data, and uses them as the initial drug treatment plan, obtains the disease type in the medical record information, obtains the medical record information of other patients with the same disease type, processes the initial drug treatment plan based on the medical record information of other patients, and generates drug recommendation information, wherein the drug recommendation information includes the occurrence frequency and cost-effectiveness of different drugs, and processes the initial drug treatment plan based on the drug recommendation information and the patient's historical physiological disease information to generate a target drug treatment plan.
[0053] In addition, the server will also obtain the patient's physiological state information and corresponding dietary information after each medication, obtain the efficacy results of each drug based on the physiological state information, perform abnormal detection on the efficacy results, generate test results, process the drugs with abnormal test results based on the dietary information, generate drug efficacy prediction information, process the target drug treatment plan based on the drug efficacy prediction information, generate the patient's treatment results, generate the effectiveness information of each target drug for the patient's pathological treatment based on the patient's treatment effect, and divide each target drug into preferred drugs and secondary drugs based on the effectiveness information. If the proportion of secondary drugs is greater than the preset threshold, generate abnormal risk reminder information.
[0054] Generate the drug resistance of each target drug at each treatment stage based on the patient's treatment results, generate the threshold of drug dependence that affects the patient based on drug resistance, generate abnormal risk reminder information if the drug dependence threshold is greater than the target threshold, generate the patient's pathological treatment prediction results based on the patient's medical record information, process the pathological treatment prediction results based on the abnormal risk reminder information, generate real-time pathological treatment results, generate new therapeutic drugs based on real-time pathological treatment results, the patient's historical physiological disease information and the target drug treatment plan, add the new therapeutic drugs to the target drug treatment plan, generate a new target drug treatment plan, process the patient based on the new target drug treatment plan, and generate drug analysis results. Generate the patient's treatment plan through the drug association model, the patient's living environment factors and the patient's medical record information, and adjust the treatment plan according to the patient's treatment results, so as to generate a target drug treatment plan suitable for the patient, further filter the drugs that may affect the patient's physical condition based on the patient's living environment factors and drug resistance, and assist medical staff in analyzing the patient's condition changes and their causes.
[0055] In one embodiment, if Figure 2 As shown, the present application also provides a medication analysis device for Parkinson's disease patients, comprising:
[0056] The acquisition module 201 is used to acquire the patient's medical record information and living environment data;
[0057] The processing module 202 is used to process the medical record information and the living environment data to generate an initial drug treatment plan; process the initial drug treatment plan based on a drug association model to generate a target drug treatment plan; process the patient based on the target drug treatment plan to generate the patient's treatment results; perform cluster analysis on the patient's treatment results to generate abnormal risk reminder information; process the patient's medical record information based on the abnormal risk reminder information to generate newly added therapeutic drugs; add the newly added therapeutic drugs to the target drug treatment plan to generate a new target drug treatment plan; process the patient based on the new target drug treatment plan to generate a medication analysis result.
[0058] In this application, the server obtains the patient's medical record information and living environment data; processes the medical record information and the living environment data to generate an initial drug treatment plan; processes the initial drug treatment plan based on the drug association model to generate a target drug treatment plan; processes the patient based on the target drug treatment plan to generate the patient's treatment results; clusters the patient's treatment results to generate abnormal risk reminder information; processes the patient's medical record information based on the abnormal risk reminder information to generate new therapeutic drugs; adds the new therapeutic drugs to the target drug treatment plan to generate a new target drug treatment plan; processes the patient based on the new target drug treatment plan to generate medication analysis results. The patient's treatment plan is generated through the drug association model, the patient's living environment factors and the patient's medical record information, and the treatment plan is adjusted according to the patient's treatment results, so as to generate a target drug treatment plan suitable for the patient, further filter the drugs that may affect the patient's physical condition based on the patient's living environment factors, and assist medical staff in analyzing the patient's condition changes and their causes.
[0059] In another embodiment of the present application, the processing module 202 is configured to process the medical record information to generate an initial drug treatment plan, including:
[0060] Obtaining therapeutic drugs corresponding to the medical record information;
[0061] Obtaining historical physiological disease information of the patient;
[0062] Obtain risk factors between different therapeutic drugs based on target drug risk model;
[0063] Appropriate therapeutic drugs are screened according to the risk factors, the historical physiological disease information and the living environment data, and used as an initial drug treatment plan.
[0064] In another embodiment of the present application, the processing module 202 is configured to process the initial drug treatment regimen based on the drug association model to generate a target drug treatment regimen, including:
[0065] The type of disease for which the medical record information is obtained;
[0066] Obtain medical records of other patients with the same disease;
[0067] Processing the initial drug treatment plan based on the medical record information of the other patients to generate drug recommendation information, wherein the drug recommendation information includes the frequency of occurrence and cost-effectiveness of different drugs;
[0068] The initial drug treatment regimen is processed based on the drug recommendation information and the patient's historical physiological disease information to generate a target drug treatment regimen.
[0069] In another embodiment of the present application, the processing module 202 is configured to process the patient based on the target drug treatment regimen to generate a treatment result for the patient, including:
[0070] Obtaining the patient's physiological state information and corresponding dietary information after each medication;
[0071] Obtaining the efficacy results of each drug based on the physiological state information;
[0072] Performing abnormality detection on the drug efficacy results to generate a detection result;
[0073] Processing the drug whose test result is in an abnormal state based on the dietary information to generate drug efficacy prediction information;
[0074] The target drug treatment regimen is processed based on the drug efficacy prediction information to generate the treatment result of the patient.
[0075] In another embodiment of the present application, the processing module 202 is configured to perform cluster analysis on the treatment results of the patient and generate abnormal risk reminder information, including:
[0076] Generate effectiveness information of each target drug on the pathological treatment of the patient based on the treatment effect of the patient;
[0077] Classifying each of the target drugs into preferred drugs and secondary drugs based on the effectiveness information;
[0078] If the proportion of the secondary drugs is greater than a preset threshold, an abnormal risk warning message is generated.
[0079] In another embodiment of the present application, the processing module 202 is configured to perform cluster analysis on the treatment results of the patient and generate abnormal risk warning information, and further includes:
[0080] generating drug resistance of each target drug at each treatment stage based on the treatment results of the patient;
[0081] generating a threshold of drug dependence affecting the patient based on the drug resistance;
[0082] If the medication dependence threshold is greater than the target threshold, abnormal risk reminder information is generated.
[0083] In another embodiment of the present application, the processing module 202 is configured to process the patient's medical record information based on the abnormal risk reminder information to generate a new therapeutic drug, including:
[0084] generating a pathology treatment prediction result for the patient based on the patient's medical record information;
[0085] Processing the pathological treatment prediction result based on the abnormal risk reminder information to generate a real-time pathological treatment result;
[0086] New therapeutic drugs are generated based on the real-time pathological treatment results, the patient's historical physiological disease information and the target drug treatment plan.
[0087] By applying the above technical solutions, the server obtains the patient's medical record information and living environment data, obtains the therapeutic drugs corresponding to the medical record information, obtains the patient's historical physiological disease information, obtains the risk factors between different therapeutic drugs based on the target drug risk model, screens suitable therapeutic drugs according to the risk factors, historical physiological disease information and living environment data, and uses them as the initial drug treatment plan, obtains the disease type in the medical record information, obtains the medical record information of other patients with the same disease type, processes the initial drug treatment plan based on the medical record information of other patients, and generates drug recommendation information, wherein the drug recommendation information includes the occurrence frequency and cost-effectiveness of different drugs, and processes the initial drug treatment plan based on the drug recommendation information and the patient's historical physiological disease information to generate a target drug treatment plan.
[0088] In addition, the server will also obtain the patient's physiological state information and corresponding dietary information after each medication, obtain the efficacy results of each drug based on the physiological state information, perform abnormal detection on the efficacy results, generate test results, process the drugs with abnormal test results based on the dietary information, generate drug efficacy prediction information, process the target drug treatment plan based on the drug efficacy prediction information, generate the patient's treatment results, generate the effectiveness information of each target drug for the patient's pathological treatment based on the patient's treatment effect, and divide each target drug into preferred drugs and secondary drugs based on the effectiveness information. If the proportion of secondary drugs is greater than the preset threshold, generate abnormal risk reminder information.
[0089] Generate the drug resistance of each target drug at each treatment stage based on the patient's treatment results, generate the threshold of drug dependence that affects the patient based on drug resistance, generate abnormal risk reminder information if the drug dependence threshold is greater than the target threshold, generate the patient's pathological treatment prediction results based on the patient's medical record information, process the pathological treatment prediction results based on the abnormal risk reminder information, generate real-time pathological treatment results, generate new therapeutic drugs based on real-time pathological treatment results, the patient's historical physiological disease information and the target drug treatment plan, add the new therapeutic drugs to the target drug treatment plan, generate a new target drug treatment plan, process the patient based on the new target drug treatment plan, and generate drug analysis results. Generate the patient's treatment plan through the drug association model, the patient's living environment factors and the patient's medical record information, and adjust the treatment plan according to the patient's treatment results, so as to generate a target drug treatment plan suitable for the patient, further filter the drugs that may affect the patient's physical condition based on the patient's living environment factors and drug resistance, and assist medical staff in analyzing the patient's condition changes and their causes.
[0090] The present application embodiment provides an electronic device, such as Figure 3 As shown, the electronic device 3 includes a first processor 300, a memory 301, a bus 302 and a communication interface 303. The first processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the first processor 300, and when the first processor 300 runs the computer program, the medication analysis method for Parkinson's patients provided in any of the aforementioned embodiments of the present application is executed.
[0091] The memory 301 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.
[0092] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store a program, and the first processor 300 executes the program after receiving an execution instruction. The medication analysis method for Parkinson's disease patients disclosed in any implementation of the embodiment of the present application may be applied to the first processor 300, or implemented by the first processor 300.
[0093] The first processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 300. The above-mentioned first processor 300 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be embodied as a hardware decoding processor to execute, or a combination of hardware and software modules in the decoding processor to execute. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the first processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.
[0094] The electronic device provided in the above-mentioned embodiment of the present application and the medication analysis method for Parkinson's disease patients provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.
[0095] The present application embodiment provides a computer-readable storage medium, such as Figure 4 As shown, the computer-readable storage medium stores 401 a computer program, and when the computer program is read and executed by the second processor 402, the aforementioned medication analysis method for Parkinson's disease patients is implemented.
[0096] The technical solution of the embodiment of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be an air conditioner, a refrigeration device, a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the method described in the embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
[0097] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the medication analysis method for Parkinson's disease patients provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.
[0098] An embodiment of the present application provides a computer program product, including a computer program, wherein the computer program is executed by a third processor to implement the method described above.
[0099] The computer program product provided in the above-mentioned embodiments of the present application and the medication analysis method for Parkinson's disease patients provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.
[0100] It should be noted that, in this application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.
[0101] Each embodiment in the present application is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the drug analysis method, electronic device, electronic device, and readable storage medium embodiment for evaluating Parkinson's disease patients, since they are basically similar to the above-mentioned drug analysis method embodiment for Parkinson's disease patients, the description is relatively simple, and the relevant parts can be referred to the partial description of the above-mentioned drug analysis method embodiment for Parkinson's disease patients.
[0102] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application shall be subject to the scope defined by the claims.
Claims
1. A medication analysis device for Parkinson's disease patients, characterized in that: The device comprises: An acquisition module is used to obtain the patient's medical record information and living environment data; a processing module, for processing the medical record information and the living environment data to generate an initial drug treatment plan; processing the initial drug treatment plan based on a drug association model to generate a target drug treatment plan; processing the patient based on the target drug treatment plan to generate a treatment result for the patient; performing cluster analysis on the treatment result of the patient to generate abnormal risk reminder information; processing the patient's medical record information based on the abnormal risk reminder information to generate a newly added therapeutic drug; adding the newly added therapeutic drug to the target drug treatment plan to generate a new target drug treatment plan; processing the patient based on the new target drug treatment plan to generate a medication analysis result; The processing of the medical record information to generate an initial drug treatment plan includes: obtaining a therapeutic drug corresponding to the medical record information; obtaining the patient's historical physiological disease information; obtaining risk factors between different therapeutic drugs based on a target drug risk model; screening suitable therapeutic drugs according to the risk factors, the historical physiological disease information and the living environment data, and using them as the initial drug treatment plan; The processing of the patient based on the target drug treatment regimen to generate the treatment result of the patient includes: obtaining the physiological state information and corresponding diet information of the patient after each medication; obtaining the efficacy result of each drug based on the physiological state information; performing abnormality detection on the efficacy result to generate the detection result; processing the drug with an abnormal detection result based on the diet information to generate drug efficacy prediction information; processing the target drug treatment regimen based on the drug efficacy prediction information to generate the treatment result of the patient; The cluster analysis of the patient's treatment results is performed to generate abnormal risk warning information, including: generating effectiveness information of each target drug for the patient's pathological treatment based on the patient's treatment effect; dividing each target drug into preferred drugs and secondary drugs based on the effectiveness information; if the proportion of the secondary drugs is greater than a preset threshold, generating abnormal risk warning information.